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Orbitz and the Macs: Signals, not segmentation

By now you've probably heard about the fact that Orbitz users accessing the site via Macs are seeing more expensive hotel options when they search. But it seems worth clearing up a couple of fallacies. First, it's not as if the same hotel room is being offered at a higher prices to Mac users. (So no, using Windows to access Orbitz won't get you a better deal.) Orbitz offers the same search results to Windows and Mac users, but Mac users may see more expensive hotels higher earlier in the search results. And the reason why this is happening isn't segmentation, as some have claimed.  From what I remember of the Orbitz presentation at the R user conference a couple of years ago, all sorts of signals go into the R model to try and predict which hotels a given Orbitz user might be interested in: WebTrends data, search history, purchase history, planned travel dates ... and the user's browser and operating system. It would seem that the browser (perhaps Safari / iOS) and/or the operating system is a strong signal that the user is likely to purchase a higher-priced hotel room. The Orbitz system (as I understand it) is designed to rank search results showing the results the specific user is most likely to be interested in at the top of the list. If Mac users, on average, spend more on hotel rooms than other Orbitz visitors, then it's not surprising that more expensive results would appear at the top of the list.  But it's not as if Mac users being singled out for special treatment. Segmentation is a marketing operation (and a conscious decision), whereas the use of signals like these is part of a predictive analytics operation (driven by data, not people). It's important to understand the distinction. You can read more about how Orbitz uses R and Hadoop to drive its search at the link below. Revolutions: How Orbitz uses Hadoop and R to optimize hotel search

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More Stories By David Smith

David Smith is Vice President of Marketing and Community at Revolution Analytics. He has a long history with the R and statistics communities. After graduating with a degree in Statistics from the University of Adelaide, South Australia, he spent four years researching statistical methodology at Lancaster University in the United Kingdom, where he also developed a number of packages for the S-PLUS statistical modeling environment. He continued his association with S-PLUS at Insightful (now TIBCO Spotfire) overseeing the product management of S-PLUS and other statistical and data mining products.<

David smith is the co-author (with Bill Venables) of the popular tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project. Today, he leads marketing for REvolution R, supports R communities worldwide, and is responsible for the Revolutions blog. Prior to joining Revolution Analytics, he served as vice president of product management at Zynchros, Inc. Follow him on twitter at @RevoDavid

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